Two-occasion Successive Sampling Research Articles

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

This paper explores some potent estimation procedures of population variance under missing at random situations in two-occasion successive sampling. Information on auxiliary variables has been involved in supporting for effective estimation of some chain-type exponential and regression estimators under the assumption of sampling units on which information of study variables cannot be obtained due to missing at random. The non-respondents follow the Binomial type of distribution in the estimation. The proposed estimators are compared with the competent estimators under a complete response of sample units. The Empirical studies support theoretical results in the present probability of non-respondent. Results have been interpreted and suitable recommendations are made for data practitioners.

Improved Estimators of Population Mean under Nonresponse in Successive Sampling

It is well accepted that suitable additional information improves the efficiency of an estimator but at the same time, it faced the situation of nonresponse. The two-occasion successive sampling is useful to handle the absence of a full response from respondents. In this present study, we have developed exponential estimators for population mean using a subsampling nonrespondent procedure. To show the efficacy of the recommended estimators, several properties are derived, and respective optimum replacement strategies are inferred. To evaluate the performance of the recommended estimators empirically, a computational study is carried out as well. It was found that the recommended estimators outperform the existing ones under the nonresponse in successive sampling.

On the use of predictive approach in estimation of population mean under successive sampling scheme

In the present article, we focus attention on the implementation of predictive approach devised by D. Basu (1971) in two-occasion successive sampling scheme. In this study one regression-type estimator of a finite population mean with the help of information on one auxiliary variable has been developed under two-occasion successive sampling using predictive approach. The pre-eminence of the suggested methodology has been established over the contemporary ones through empirical studies carried over a real population and artificially generated population datasets, which presents the soundness and usefulness of the suggested estimator in practice. Suitable recommendations to the survey statisticians are also made.

Improved imputation methods for missing data in two-occasion successive sampling

Missing data often complicates survey practitioners to construct reliable estimates of the required population parameters. Remembering this fact and motivated with the recent work, this article deals with some imputation methods to handle the missing data problem at the beginning of the analysis and some estimation procedures of the current population mean have been proposed in two-occasion successive sampling. The expressions for the bias, mean squared error and optimum mean squared error are derived using the concept of large sample approximations. The empirical performances are shown over the similar type of estimators designated for the complete response situation and over recently developed estimator which are defined for the situation when missing observations are observed in the sample data. Suitable recommendations have been made for survey researchers.

Investigation of Competent Estimation Procedure for Estimating the Finite Population Mean at Current Occasion Addressing Non-response in Two-Occasion Successive Sampling

This paper is an effort to investigate the effect of non-response at the current (second) occasion in successive sampling. Using the subsampling non-respondent procedure, some exponential-type estimators have been proposed to estimate the current population mean. Properties of the proposed class of estimators are examined, and respective optimum replacement strategies are derived. Empirical studies are carried out to evaluate the performances of the suggested estimators. The outcomes are interpreted, and appropriate recommendations have been made.

Some imputation methods to deal with the issue of missing data problems due to random non-response in two-occasion successive sampling

Missingness or incompleteness of data is a challenge to the survey statisticians in producing the reliable estimates of the desired population parameters. If the missingness pattern is unidentifiable, this is the case of random non-response and to deal with such situations, this work proposes some alternative imputations methods to cope with the missing data. The proposed imputation methods result in some efficient estimation procedures of the current population mean in two-occasion successive sampling. The properties of the resultant estimation procedures have been examined and supplemented with empirical studies. Results have been critically analyzed, and recommendations are made to the survey practitioners.

General Estimation Procedures of Population Mean in Two-Occasion Successive Sampling under Random Non-response

In this paper, an attempt has been made to minimize the negative impact of random non-response in the estimation of population mean on current occasion in two-occasion successive sampling. A general class of estimators has been proposed, and their properties are studied under the assumptions that the number of sampling unit on which information may not be obtained follows a discrete probability distribution due to random non-response behavior. Properties of the proposed class of estimators are also derived for as individual cases when random non-response occurs either on the first or the second occasion. Empirical study has been carried out to show the efficacious performances of the proposed class of estimators over an estimator defined for the complete response situations. Suitable recommendations have been made to the applied survey practitioners for their real-life practical applications.

A composite class of estimators to deal with the issue of variance estimation under the situations of random non-response in two-occasion successive sampling

Missing data often creates complications in data analysis for any type of scientific investigations. The non response is the source of missing data and it is an unavoidable phenomenon in survey sampling. This problem becomes more severe in the case of repeated surveys because of its repetitious nature. The missing data due to random causes which we call as random non-response is an important issue which needs to be dealt with a more cautious approach. To address these thoughts, this study is an attempt to reduce the adverse impact of random non response in the estimation of population variance on the current occasion in two-occasion successive sampling. A composite class of estimators for estimation of population variance on the current occasion has been proposed and its properties are deeply examined. An empirical study is carried out to show the efficacious performances of the proposed class of estimators over an estimator defined for the complete response situation. Suitable recommendations are made to the survey practitioners for their real-life practical applications.

Formulation of logarithmic type estimators to estimate population mean in successive sampling in presence of random non response and measurement errors

This paper considers the problem of estimation of population mean of the study character in two-occasion successive sampling in presence of non-response and measurement error. A logarithmic type imputation technique has been developed to reduce the nuisance effect of non-response in sample surveys. Utilizing information available on a highly positively or highly negatively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. An optimal replacement policy has been derived, and effect of measurement errors on the mean square error of the estimator has been studied. Empirical studies have been carried out using both real and simulated data, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems.

Some imputation methods to compensate with non-response for estimation of population mean in two-occasion successive sampling

Non-response is an unavoidable phenomenon in almost all sample survey, and if it is not dealt carefully at the very starting of the analysis, results may lead to significant biases in the survey estimates. Keeping these facts in mind and inspired by the work of Singh (2009), this article suggests some alternative efficient methods to deal with the missing data problems at the beginning of the analysis and proposes estimation procedures of the current population mean in two-occasion successive sampling. The properties of the resultant estimation procedures have been examined and supplemented with empirical studies. Results have been critically analyzed, and recommendations are made to the survey practitioners.

Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

Some imputation methods to deal with the problems of missing data in two-occasion successive sampling

The missing data often create nuisance to the survey practitioners in producing the reliable estimates of the desired parameters. Keeping this point in view, the present work suggests some alternative imputation techniques to deal with the missing data problem at the beginning of the analysis and proposes some estimation procedures of current population mean in two-occasion successive sampling. The properties of the suggested estimation procedures have been analyzed, and their empirical performances are compared with similar type of estimators designated for whole response situation and another estimator defined for the situation when missing observations observed in the sample data. Based on the fascinating results, suitable recommendations are put forward to the survey practitioners/researchers for their real-life practical applications.

Effective Estimation Strategy of Population Variance in Two-Phase Successive Sampling Under Random Non-response

In this paper, an attempt has been made to present the problem of estimation of current population variance in the presence of random non-response in two-occasion successive sampling under two-phase setup. The properties of the proposed estimation procedures are deeply examined with the assumption that numbers of sampling units follow a distribution owing to random non-response. The performances of the proposed estimators are compared with the estimators designated for the complete response situations. We have discussed the effectiveness of the proposed estimators through the results that are interpreted by empirical studies. Appropriate recommendations have been made to the survey practitioners/researchers for their real-life practical applications.

Estimation of population mean in successive sampling under super-population model in presence of random non response situations

The present work is an attempt to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling in presence of random non response situations. The estimation strategy has been constructed under a super-population model design approach with the help of imputation technique. The estimators proposed on the current occasion cover the cases of occurrences random non responses on either of the occasions. Detail behaviors of the proposed class of estimators have been studied and its performance has been examined with the sample mean estimator. The results are demonstrated through empirical studies which establish the effectiveness of the proposed class of estimators. Suitable recommendations have been put forward to the survey statisticians for its practical application.

Estimation of current population mean using two-occasion successive sampling with one auxiliary variable

In this study, two-occasion successive sampling for ratio-to-regression estimator was used to determine the current estimate of the population mean using only the matched part and one auxiliary variable, which is available on both the occasions. The data used were based on the total number of female workers in villages in Mizoram with the total number of literate female in villages in Mizoram as an auxiliary variables. The data were gotten from Census of India 2001 and 2011. The optimum mean square error of the combined ratio-to-regression and ratio estimator has been compared with (i) the optimum mean square error of the chain-type ratio estimator (ii) mean per unit estimator and (iii) combined estimator when no auxiliary information is used at any occasion. This result showed that the combined ratio-to-regression and ratio estimator is more efficient than the other three existing estimators.

Hunt for efficient variance estimation strategy in two-occasion successive sampling using fuzzy tools

The purpose of this paper is to report a general group of estimators for population variance estimation on second (current) occasion in two–occasion rotation sampling. Behaviors of the introduced class of estimators are studied in detail and its optimum replacement policy has also been observed. The introduced class of estimators has been compared with the natural estimator of the population variance and the results obtained are demonstrated empirically. The dominance ranges of the proposed estimation strategies are identified and illustrated through fuzzification tools followed by suitable recommendations.

An effective estimation strategy for population mean under random non-response situations in two-phase successive sampling

ABSTRACTThis paper presents a modified exponential type estimation strategy for the current population mean in the presence of random non-response situations in two-occasion successive sampling under two-phase set-up. The properties of the proposed estimators have been examined with the assumption that numbers of sampling units follow a distribution due to random non-response. The performances of the proposed estimators are compared with the estimators designated for the complete response situations. Empirical studies are carried out to show the dominance nature of the proposed estimators over the estimator defined for complete response situations. Appropriate recommendations have been made to the survey practitioners/researchers for their real-life practical applications.

IMPROVED ROTATION PATTERNS USING TWO AUXILIARY VARIABLES IN SUCCESSIVE SAMPLING

Abstract The present paper emphasizes the role of two auxiliary variables on both the occasions to improve the precision of estimates at the current (second) occasion in two-occasion successive sampling. Information on two auxiliary variables, which are positively correlated with the study variable, has been used with the aid of exponential type structures and an efficient estimation procedure of population mean on the current (second) occasion has been suggested. The behaviour of the proposed estimator has been studied and compared with the sample mean estimator, when there is no matching from the previous occasion and natural successive sampling estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current (second) occasion. Optimal replacement strategy is also discussed. The concluding remarks are discussed justifying utility of the proposed sampling scheme. The results have been well supported analytically as well as empirically by using real life data.

Some estimation procedures of sensitive character using scrambled response techniques in successive sampling

ABSTRACTThis paper is an attempt to analyze the effect of scrambled response techniques to estimate the current population mean in two-occasion successive sampling when study and auxiliary variables are sensitive in nature. Using additive and multiplicative scrambled response models on both occasions, some generalized estimation strategies of current population mean have been proposed and their properties are examined. Empirical studies are performed on real data (abortion rate in the states of United States) and simulation studies are also carried out to evaluate the performances of the proposed estimators over other estimators. Results have proved the worthiness of the scrambled response techniques in real-life situations.

Efficient Class of Estimators of Population Variance Under Random Non-response in Two-Phase Successive Sampling

In this paper, an investigation has been carried out to deal with the problems of non-response in estimation of population variance on current occasion in two-occasion successive sampling under two-phase setup. Using readily available information on an auxiliary variable on both occasions, class of estimators of population variance on current occasion have been proposed in presence of random non-response on either of the occasions. Performances of the proposed class of estimators are compared with sample variance estimator under the similar realistic situations to study the properties of the suggested estimation procedures under missing at random response mechanism. Results are validated through empirical studies which justify the proposition of the class of estimators. Suitable recommendations have been made to the survey practitioners.